DBMS > Heroic vs. Machbase Neo vs. Microsoft Azure Cosmos DB vs. Oracle vs. Qdrant

System Properties Comparison Heroic vs. Machbase Neo vs. Microsoft Azure Cosmos DB vs. Oracle vs. Qdrant

Editorial information provided by DB-Engines

Name

Description

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

TimeSeries DBMS for AIoT and BigData

Globally distributed, horizontally scalable, multi-model database service

Widely used RDBMS

A high-performance vector database with neural network or semantic-based matching

Primary database model

Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Vector DBMS

Secondary database models

Spatial DBMS

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	0.17
Rank	#337	Overall
	#30	Time Series DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	1244.08
Rank	#1	Overall
	#1	Relational DBMS

Score	1.28
Rank	#167	Overall
	#6	Vector DBMS

Website

github.com/spotify/heroic

machbase.com

azure.microsoft.com/services/cosmos-db

www.oracle.com/database

github.com/qdrant/qdrant
qdrant.tech

Technical documentation

spotify.github.io/heroic

machbase.com/dbms

learn.microsoft.com/azure/cosmos-db

docs.oracle.com/en/database

qdrant.tech/documentation

Developer

Spotify

Machbase

Microsoft

Oracle

Qdrant

Initial release

2014

2013

2014

1980

2021

Current release

V8.0, August 2023

23c, September 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

free test version available

commercial

restricted free version is available

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C and C++

Rust

Server operating systems

Linux
macOS
Windows

hosted

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

Docker
Linux
macOS
Windows

Data scheme

schema-free

yes

schema-free

yes

Schemaless in JSON and XML columns

schema-free

Typing

predefined data types such as float or date

yes

JSON types

yes

Numbers, Strings, Geo, Boolean

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

yes

via Elasticsearch

yes

All properties auto-indexed by default

yes

Keywords, numberic ranges, geo, full-text

SQL

Support of SQL

SQL-like query language

yes

with proprietary extensions

APIs and other access methods

HQL (Heroic Query Language, a JSON-based language)
HTTP API

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

Supported programming languages

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

.Net
Go
Java
JavaScript (Node.js)
Python
Rust

Server-side scripts

Stored procedures

JavaScript

PL/SQL

also stored procedures in Java possible

Triggers

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding, horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

yes

Implicit feature of the cloud service

Multi-source replication
Source-replica replication

Collection-level replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

can be realized in PL/SQL

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Eventual Consistency, tunable consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Multi-item ACID transactions with snapshot isolation within a partition

ACID

isolation level can be parameterized

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

volatile and lookup table

yes

Version 12c introduced the new option 'Oracle Database In-Memory'

yes

User concepts

Access control

simple password-based access control

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

Key-based authentication

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